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Ethical challenges of AI-based deception detection in forensic settings.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Ethical challenges of AI-based deception detection in forensic settings.
Συγγραφείς: Fumagalli, Anita
Πηγή: Assessment & Development Matters; Autumn2026, Vol. 18 Issue 3, p17-23, 7p
Θεματικοί όροι: Lie detectors & detection, Ethics, Artificial intelligence, Cultural awareness, False positive error, Statistical bias, Forensic sciences, Test validity
Περίληψη: Key digested message Artificial intelligence for deception detection shows promise but raises significant scientific and ethical concerns in forensic settings. Evidence suggests that many systems perform well in controlled studies but lack real-world validity due to bias, limited theoretical grounding, and dataset constraints. They may also be opaque, culturally insensitive, and prone to reinforcing errors and false positives in high-stakes forensic decisions. Therefore, caution is recommended. [ABSTRACT FROM AUTHOR]
Copyright of Assessment & Development Matters is the property of British Psychological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Ethical challenges of AI-based deception detection in forensic settings.
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  Data: Assessment & Development Matters; Autumn2026, Vol. 18 Issue 3, p17-23, 7p
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  Data: Key digested message Artificial intelligence for deception detection shows promise but raises significant scientific and ethical concerns in forensic settings. Evidence suggests that many systems perform well in controlled studies but lack real-world validity due to bias, limited theoretical grounding, and dataset constraints. They may also be opaque, culturally insensitive, and prone to reinforcing errors and false positives in high-stakes forensic decisions. Therefore, caution is recommended. [ABSTRACT FROM AUTHOR]
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  Label:
  Group: Ab
  Data: <i>Copyright of Assessment & Development Matters is the property of British Psychological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.53841/bpsadm.2026.18.3.17
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      – Code: eng
        Text: English
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      – SubjectFull: Cultural awareness
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      – SubjectFull: False positive error
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      – SubjectFull: Statistical bias
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      – SubjectFull: Forensic sciences
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      – SubjectFull: Test validity
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      – TitleFull: Ethical challenges of AI-based deception detection in forensic settings.
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              Text: Autumn2026
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              Y: 2026
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